Effects of spectrometer band pass, sampling, and signalto-noise ratio on spectral identification using the Tetracorder algorithm

Estimates of spectrometer bandpass, sampling interval, and signal-to-noise ratio required for identification of pure minerals and plants were derived using reflectance spectra convolved to AVIRIS, HYDICE, MIVIS, VIMS, and other imaging spectrometers. For each spectral simulation, various levels of random noise were added to the reflectance spectra after convolution and then each was analyzed with the Tetracorder spectral identification algorithm (Clark et al., 2003). The outcome of each identification attempt was tabulated to provide an estimate of the signal-to-noise ratio at which a given percentage of the noisy spectra were identified correctly. Results show that spectral identification is most sensitive to the signal-to-noise ratio at narrow sampling interval values but is more sensitive to the sampling interval itself at broad sampling interval values because of spectral aliasing – a condition when absorption features of different materials can resemble one another. The bandpass is less critical to spectral identification than the sampling interval or signal-to-noise ratio because broadening the bandpas